Overview

Dataset statistics

Number of variables23
Number of observations158
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.6 KiB
Average record size in memory192.0 B

Variable types

Numeric16
Categorical7

Alerts

Card_Category has constant value ""Constant
Customer_Age is highly overall correlated with Months_on_bookHigh correlation
Months_on_book is highly overall correlated with Customer_AgeHigh correlation
Months_Inactive_12_mon is highly overall correlated with Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1 and 1 other fieldsHigh correlation
Contacts_Count_12_mon is highly overall correlated with Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1 and 1 other fieldsHigh correlation
Credit_Limit is highly overall correlated with Avg_Open_To_Buy and 1 other fieldsHigh correlation
Total_Revolving_Bal is highly overall correlated with Attrition_FlagHigh correlation
Avg_Open_To_Buy is highly overall correlated with Credit_Limit and 1 other fieldsHigh correlation
Total_Trans_Amt is highly overall correlated with Total_Trans_CtHigh correlation
Total_Trans_Ct is highly overall correlated with Total_Trans_Amt and 1 other fieldsHigh correlation
Avg_Utilization_Ratio is highly overall correlated with Credit_Limit and 1 other fieldsHigh correlation
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1 is highly overall correlated with Months_Inactive_12_mon and 3 other fieldsHigh correlation
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2 is highly overall correlated with Months_Inactive_12_mon and 3 other fieldsHigh correlation
Attrition_Flag is highly overall correlated with Total_Revolving_Bal and 3 other fieldsHigh correlation
Gender is highly overall correlated with Income_CategoryHigh correlation
Income_Category is highly overall correlated with GenderHigh correlation
CLIENTNUM has unique valuesUnique
Dependent_count has 15 (9.5%) zerosZeros
Contacts_Count_12_mon has 10 (6.3%) zerosZeros

Reproduction

Analysis started2023-11-07 10:20:20.977061
Analysis finished2023-11-07 10:20:46.726981
Duration25.75 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

CLIENTNUM
Real number (ℝ)

UNIQUE 

Distinct158
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4247182 × 108
Minimum7.0839701 × 108
Maximum8.2828833 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:46.862169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum7.0839701 × 108
5-th percentile7.098795 × 108
Q17.1410218 × 108
median7.177149 × 108
Q37.7950177 × 108
95-th percentile8.190501 × 108
Maximum8.2828833 × 108
Range1.1989132 × 108
Interquartile range (IQR)65399588

Descriptive statistics

Standard deviation39299580
Coefficient of variation (CV)0.052930737
Kurtosis-0.84528214
Mean7.4247182 × 108
Median Absolute Deviation (MAD)5567212.5
Skewness0.89019404
Sum1.1731055 × 1011
Variance1.544457 × 1015
MonotonicityNot monotonic
2023-11-07T13:20:47.001365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
715190283 1
 
0.6%
712613808 1
 
0.6%
820868508 1
 
0.6%
789095958 1
 
0.6%
708397008 1
 
0.6%
779405133 1
 
0.6%
789907533 1
 
0.6%
823908858 1
 
0.6%
716689383 1
 
0.6%
715418508 1
 
0.6%
Other values (148) 148
93.7%
ValueCountFrequency (%)
708397008 1
0.6%
708426483 1
0.6%
708702258 1
0.6%
708880683 1
0.6%
709094358 1
0.6%
709273383 1
0.6%
709310433 1
0.6%
709788783 1
0.6%
709895508 1
0.6%
710357208 1
0.6%
ValueCountFrequency (%)
828288333 1
0.6%
827901183 1
0.6%
826548708 1
0.6%
823928058 1
0.6%
823908858 1
0.6%
823621083 1
0.6%
822138333 1
0.6%
820868508 1
0.6%
818729208 1
0.6%
818375733 1
0.6%

Attrition_Flag
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Existing Customer
127 
Attrited Customer
31 

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters2686
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExisting Customer
2nd rowExisting Customer
3rd rowExisting Customer
4th rowAttrited Customer
5th rowExisting Customer

Common Values

ValueCountFrequency (%)
Existing Customer 127
80.4%
Attrited Customer 31
 
19.6%

Length

2023-11-07T13:20:47.114513image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T13:20:47.241200image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
customer 158
50.0%
existing 127
40.2%
attrited 31
 
9.8%

Most occurring characters

ValueCountFrequency (%)
t 378
14.1%
i 285
10.6%
s 285
10.6%
e 189
 
7.0%
r 189
 
7.0%
158
 
5.9%
C 158
 
5.9%
u 158
 
5.9%
o 158
 
5.9%
m 158
 
5.9%
Other values (6) 570
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2212
82.4%
Uppercase Letter 316
 
11.8%
Space Separator 158
 
5.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 378
17.1%
i 285
12.9%
s 285
12.9%
e 189
8.5%
r 189
8.5%
u 158
7.1%
o 158
7.1%
m 158
7.1%
x 127
 
5.7%
n 127
 
5.7%
Other values (2) 158
7.1%
Uppercase Letter
ValueCountFrequency (%)
C 158
50.0%
E 127
40.2%
A 31
 
9.8%
Space Separator
ValueCountFrequency (%)
158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2528
94.1%
Common 158
 
5.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 378
15.0%
i 285
11.3%
s 285
11.3%
e 189
7.5%
r 189
7.5%
C 158
 
6.2%
u 158
 
6.2%
o 158
 
6.2%
m 158
 
6.2%
E 127
 
5.0%
Other values (5) 443
17.5%
Common
ValueCountFrequency (%)
158
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2686
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 378
14.1%
i 285
10.6%
s 285
10.6%
e 189
 
7.0%
r 189
 
7.0%
158
 
5.9%
C 158
 
5.9%
u 158
 
5.9%
o 158
 
5.9%
m 158
 
5.9%
Other values (6) 570
21.2%

Customer_Age
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.829114
Minimum30
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:47.343995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile35.85
Q143
median48
Q352
95-th percentile61
Maximum65
Range35
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.4021799
Coefficient of variation (CV)0.15476306
Kurtosis-0.19481356
Mean47.829114
Median Absolute Deviation (MAD)5
Skewness0.073355251
Sum7557
Variance54.792268
MonotonicityNot monotonic
2023-11-07T13:20:47.478425image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
51 14
 
8.9%
45 13
 
8.2%
50 10
 
6.3%
47 9
 
5.7%
49 9
 
5.7%
48 8
 
5.1%
54 7
 
4.4%
37 7
 
4.4%
56 7
 
4.4%
44 6
 
3.8%
Other values (23) 68
43.0%
ValueCountFrequency (%)
30 1
 
0.6%
31 1
 
0.6%
34 4
2.5%
35 2
 
1.3%
36 2
 
1.3%
37 7
4.4%
38 2
 
1.3%
39 4
2.5%
40 4
2.5%
41 3
1.9%
ValueCountFrequency (%)
65 2
 
1.3%
64 3
1.9%
63 1
 
0.6%
62 1
 
0.6%
61 3
1.9%
60 1
 
0.6%
59 3
1.9%
57 2
 
1.3%
56 7
4.4%
55 5
3.2%

Gender
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
F
86 
M
72 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters158
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
F 86
54.4%
M 72
45.6%

Length

2023-11-07T13:20:47.589020image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T13:20:47.716113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
f 86
54.4%
m 72
45.6%

Most occurring characters

ValueCountFrequency (%)
F 86
54.4%
M 72
45.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 158
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 86
54.4%
M 72
45.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 158
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 86
54.4%
M 72
45.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 86
54.4%
M 72
45.6%

Dependent_count
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4113924
Minimum0
Maximum5
Zeros15
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:47.842429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2675758
Coefficient of variation (CV)0.52566135
Kurtosis-0.38155799
Mean2.4113924
Median Absolute Deviation (MAD)1
Skewness-0.14992776
Sum381
Variance1.6067484
MonotonicityNot monotonic
2023-11-07T13:20:47.977249image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 49
31.0%
2 46
29.1%
4 22
13.9%
1 19
 
12.0%
0 15
 
9.5%
5 7
 
4.4%
ValueCountFrequency (%)
0 15
 
9.5%
1 19
 
12.0%
2 46
29.1%
3 49
31.0%
4 22
13.9%
5 7
 
4.4%
ValueCountFrequency (%)
5 7
 
4.4%
4 22
13.9%
3 49
31.0%
2 46
29.1%
1 19
 
12.0%
0 15
 
9.5%

Education_Level
Categorical

Distinct5
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Graduate
78 
Uneducated
29 
Unknown
25 
College
15 
Doctorate
11 

Length

Max length10
Median length9
Mean length8.1835443
Min length7

Characters and Unicode

Total characters1293
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduate
2nd rowGraduate
3rd rowUneducated
4th rowUneducated
5th rowUnknown

Common Values

ValueCountFrequency (%)
Graduate 78
49.4%
Uneducated 29
 
18.4%
Unknown 25
 
15.8%
College 15
 
9.5%
Doctorate 11
 
7.0%

Length

2023-11-07T13:20:48.103380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T13:20:48.202256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
graduate 78
49.4%
uneducated 29
 
18.4%
unknown 25
 
15.8%
college 15
 
9.5%
doctorate 11
 
7.0%

Most occurring characters

ValueCountFrequency (%)
a 196
15.2%
e 177
13.7%
d 136
10.5%
t 129
10.0%
u 107
8.3%
n 104
8.0%
r 89
6.9%
G 78
 
6.0%
o 62
 
4.8%
U 54
 
4.2%
Other values (7) 161
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1135
87.8%
Uppercase Letter 158
 
12.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 196
17.3%
e 177
15.6%
d 136
12.0%
t 129
11.4%
u 107
9.4%
n 104
9.2%
r 89
7.8%
o 62
 
5.5%
c 40
 
3.5%
l 30
 
2.6%
Other values (3) 65
 
5.7%
Uppercase Letter
ValueCountFrequency (%)
G 78
49.4%
U 54
34.2%
C 15
 
9.5%
D 11
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1293
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 196
15.2%
e 177
13.7%
d 136
10.5%
t 129
10.0%
u 107
8.3%
n 104
8.0%
r 89
6.9%
G 78
 
6.0%
o 62
 
4.8%
U 54
 
4.2%
Other values (7) 161
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 196
15.2%
e 177
13.7%
d 136
10.5%
t 129
10.0%
u 107
8.3%
n 104
8.0%
r 89
6.9%
G 78
 
6.0%
o 62
 
4.8%
U 54
 
4.2%
Other values (7) 161
12.5%

Marital_Status
Categorical

Distinct4
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Single
73 
Married
68 
Unknown
11 
Divorced
 
6

Length

Max length8
Median length7.5
Mean length6.5759494
Min length6

Characters and Unicode

Total characters1039
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowMarried
3rd rowSingle
4th rowSingle
5th rowMarried

Common Values

ValueCountFrequency (%)
Single 73
46.2%
Married 68
43.0%
Unknown 11
 
7.0%
Divorced 6
 
3.8%

Length

2023-11-07T13:20:48.324357image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T13:20:48.426794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
single 73
46.2%
married 68
43.0%
unknown 11
 
7.0%
divorced 6
 
3.8%

Most occurring characters

ValueCountFrequency (%)
e 147
14.1%
i 147
14.1%
r 142
13.7%
n 106
10.2%
d 74
7.1%
S 73
7.0%
l 73
7.0%
g 73
7.0%
M 68
6.5%
a 68
6.5%
Other values (7) 68
6.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 881
84.8%
Uppercase Letter 158
 
15.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 147
16.7%
i 147
16.7%
r 142
16.1%
n 106
12.0%
d 74
8.4%
l 73
8.3%
g 73
8.3%
a 68
7.7%
o 17
 
1.9%
k 11
 
1.2%
Other values (3) 23
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
S 73
46.2%
M 68
43.0%
U 11
 
7.0%
D 6
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 1039
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 147
14.1%
i 147
14.1%
r 142
13.7%
n 106
10.2%
d 74
7.1%
S 73
7.0%
l 73
7.0%
g 73
7.0%
M 68
6.5%
a 68
6.5%
Other values (7) 68
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1039
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 147
14.1%
i 147
14.1%
r 142
13.7%
n 106
10.2%
d 74
7.1%
S 73
7.0%
l 73
7.0%
g 73
7.0%
M 68
6.5%
a 68
6.5%
Other values (7) 68
6.5%

Income_Category
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Less than $40K
51 
$40K - $60K
33 
$60K - $80K
30 
Unknown
22 
$80K - $120K
14 

Length

Max length14
Median length12
Mean length11.297468
Min length7

Characters and Unicode

Total characters1785
Distinct characters22
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$40K - $60K
2nd rowUnknown
3rd row$40K - $60K
4th rowUnknown
5th row$80K - $120K

Common Values

ValueCountFrequency (%)
Less than $40K 51
32.3%
$40K - $60K 33
20.9%
$60K - $80K 30
19.0%
Unknown 22
13.9%
$80K - $120K 14
 
8.9%
$120K + 8
 
5.1%

Length

2023-11-07T13:20:48.536635image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T13:20:48.774980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
85
20.1%
40k 84
19.9%
60k 63
14.9%
less 51
12.1%
than 51
12.1%
80k 44
10.4%
unknown 22
 
5.2%
120k 22
 
5.2%

Most occurring characters

ValueCountFrequency (%)
264
14.8%
K 213
11.9%
0 213
11.9%
$ 213
11.9%
n 117
 
6.6%
s 102
 
5.7%
4 84
 
4.7%
- 77
 
4.3%
6 63
 
3.5%
e 51
 
2.9%
Other values (12) 388
21.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 489
27.4%
Decimal Number 448
25.1%
Uppercase Letter 286
16.0%
Space Separator 264
14.8%
Currency Symbol 213
11.9%
Dash Punctuation 77
 
4.3%
Math Symbol 8
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 117
23.9%
s 102
20.9%
e 51
10.4%
a 51
10.4%
h 51
10.4%
t 51
10.4%
k 22
 
4.5%
o 22
 
4.5%
w 22
 
4.5%
Decimal Number
ValueCountFrequency (%)
0 213
47.5%
4 84
 
18.8%
6 63
 
14.1%
8 44
 
9.8%
1 22
 
4.9%
2 22
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
K 213
74.5%
L 51
 
17.8%
U 22
 
7.7%
Space Separator
ValueCountFrequency (%)
264
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 213
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Math Symbol
ValueCountFrequency (%)
+ 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1010
56.6%
Latin 775
43.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
K 213
27.5%
n 117
15.1%
s 102
13.2%
e 51
 
6.6%
L 51
 
6.6%
a 51
 
6.6%
h 51
 
6.6%
t 51
 
6.6%
U 22
 
2.8%
k 22
 
2.8%
Other values (2) 44
 
5.7%
Common
ValueCountFrequency (%)
264
26.1%
0 213
21.1%
$ 213
21.1%
4 84
 
8.3%
- 77
 
7.6%
6 63
 
6.2%
8 44
 
4.4%
1 22
 
2.2%
2 22
 
2.2%
+ 8
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1785
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
264
14.8%
K 213
11.9%
0 213
11.9%
$ 213
11.9%
n 117
 
6.6%
s 102
 
5.7%
4 84
 
4.7%
- 77
 
4.3%
6 63
 
3.5%
e 51
 
2.9%
Other values (12) 388
21.7%

Card_Category
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
Blue
158 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters632
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlue
2nd rowBlue
3rd rowBlue
4th rowBlue
5th rowBlue

Common Values

ValueCountFrequency (%)
Blue 158
100.0%

Length

2023-11-07T13:20:48.924811image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T13:20:49.012039image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
blue 158
100.0%

Most occurring characters

ValueCountFrequency (%)
B 158
25.0%
l 158
25.0%
u 158
25.0%
e 158
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 474
75.0%
Uppercase Letter 158
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 158
33.3%
u 158
33.3%
e 158
33.3%
Uppercase Letter
ValueCountFrequency (%)
B 158
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 632
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 158
25.0%
l 158
25.0%
u 158
25.0%
e 158
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 158
25.0%
l 158
25.0%
u 158
25.0%
e 158
25.0%

Months_on_book
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.563291
Minimum13
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:49.122345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile24.85
Q133
median36
Q342
95-th percentile51
Maximum56
Range43
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.040846
Coefficient of variation (CV)0.21406128
Kurtosis0.24368305
Mean37.563291
Median Absolute Deviation (MAD)5
Skewness-0.0011778359
Sum5935
Variance64.655204
MonotonicityNot monotonic
2023-11-07T13:20:49.280549image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
36 30
19.0%
40 10
 
6.3%
41 10
 
6.3%
46 8
 
5.1%
35 8
 
5.1%
39 7
 
4.4%
43 6
 
3.8%
32 6
 
3.8%
28 6
 
3.8%
33 5
 
3.2%
Other values (24) 62
39.2%
ValueCountFrequency (%)
13 1
 
0.6%
19 1
 
0.6%
20 1
 
0.6%
22 4
2.5%
24 1
 
0.6%
25 2
 
1.3%
26 3
1.9%
27 3
1.9%
28 6
3.8%
29 4
2.5%
ValueCountFrequency (%)
56 5
3.2%
54 2
 
1.3%
51 2
 
1.3%
50 2
 
1.3%
49 2
 
1.3%
48 3
 
1.9%
47 5
3.2%
46 8
5.1%
45 3
 
1.9%
44 1
 
0.6%
Distinct3
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
3
70 
4
50 
2
38 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters158
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 70
44.3%
4 50
31.6%
2 38
24.1%

Length

2023-11-07T13:20:49.406239image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T13:20:49.509030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
3 70
44.3%
4 50
31.6%
2 38
24.1%

Most occurring characters

ValueCountFrequency (%)
3 70
44.3%
4 50
31.6%
2 38
24.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 158
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 70
44.3%
4 50
31.6%
2 38
24.1%

Most occurring scripts

ValueCountFrequency (%)
Common 158
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 70
44.3%
4 50
31.6%
2 38
24.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 70
44.3%
4 50
31.6%
2 38
24.1%

Months_Inactive_12_mon
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4367089
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:49.610166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4.15
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1083559
Coefficient of variation (CV)0.45485774
Kurtosis1.8342674
Mean2.4367089
Median Absolute Deviation (MAD)1
Skewness1.0004781
Sum385
Variance1.2284528
MonotonicityNot monotonic
2023-11-07T13:20:49.715488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 60
38.0%
2 52
32.9%
1 32
20.3%
4 6
 
3.8%
6 5
 
3.2%
5 3
 
1.9%
ValueCountFrequency (%)
1 32
20.3%
2 52
32.9%
3 60
38.0%
4 6
 
3.8%
5 3
 
1.9%
6 5
 
3.2%
ValueCountFrequency (%)
6 5
 
3.2%
5 3
 
1.9%
4 6
 
3.8%
3 60
38.0%
2 52
32.9%
1 32
20.3%

Contacts_Count_12_mon
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4746835
Minimum0
Maximum5
Zeros10
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:49.825288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1269674
Coefficient of variation (CV)0.45539861
Kurtosis-0.23726594
Mean2.4746835
Median Absolute Deviation (MAD)1
Skewness-0.42300737
Sum391
Variance1.2700556
MonotonicityNot monotonic
2023-11-07T13:20:49.919588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 61
38.6%
2 41
25.9%
4 24
 
15.2%
1 20
 
12.7%
0 10
 
6.3%
5 2
 
1.3%
ValueCountFrequency (%)
0 10
 
6.3%
1 20
 
12.7%
2 41
25.9%
3 61
38.6%
4 24
 
15.2%
5 2
 
1.3%
ValueCountFrequency (%)
5 2
 
1.3%
4 24
 
15.2%
3 61
38.6%
2 41
25.9%
1 20
 
12.7%
0 10
 
6.3%

Credit_Limit
Real number (ℝ)

HIGH CORRELATION 

Distinct157
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5913.6392
Minimum3521
Maximum9397
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:50.032512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3521
5-th percentile3658.35
Q14216
median5614.5
Q37319.25
95-th percentile8995.6
Maximum9397
Range5876
Interquartile range (IQR)3103.25

Descriptive statistics

Standard deviation1793.0355
Coefficient of variation (CV)0.3032034
Kurtosis-1.1847606
Mean5913.6392
Median Absolute Deviation (MAD)1500.5
Skewness0.36928355
Sum934355
Variance3214976.3
MonotonicityNot monotonic
2023-11-07T13:20:50.169782image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7169 2
 
1.3%
3672 1
 
0.6%
4170 1
 
0.6%
7106 1
 
0.6%
7603 1
 
0.6%
3805 1
 
0.6%
5716 1
 
0.6%
4576 1
 
0.6%
3861 1
 
0.6%
3649 1
 
0.6%
Other values (147) 147
93.0%
ValueCountFrequency (%)
3521 1
0.6%
3532 1
0.6%
3540 1
0.6%
3585 1
0.6%
3626 1
0.6%
3640 1
0.6%
3642 1
0.6%
3649 1
0.6%
3660 1
0.6%
3667 1
0.6%
ValueCountFrequency (%)
9397 1
0.6%
9317 1
0.6%
9300 1
0.6%
9227 1
0.6%
9204 1
0.6%
9148 1
0.6%
9104 1
0.6%
9033 1
0.6%
8989 1
0.6%
8900 1
0.6%

Total_Revolving_Bal
Real number (ℝ)

HIGH CORRELATION 

Distinct132
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean763.06329
Minimum145
Maximum996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:50.284890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum145
5-th percentile408
Q1662.25
median794
Q3911.25
95-th percentile981.6
Maximum996
Range851
Interquartile range (IQR)249

Descriptive statistics

Standard deviation182.1388
Coefficient of variation (CV)0.23869423
Kurtosis1.2491673
Mean763.06329
Median Absolute Deviation (MAD)121
Skewness-1.0813498
Sum120564
Variance33174.544
MonotonicityNot monotonic
2023-11-07T13:20:50.547219image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
795 3
 
1.9%
622 3
 
1.9%
719 2
 
1.3%
915 2
 
1.3%
847 2
 
1.3%
561 2
 
1.3%
688 2
 
1.3%
938 2
 
1.3%
947 2
 
1.3%
961 2
 
1.3%
Other values (122) 136
86.1%
ValueCountFrequency (%)
145 1
0.6%
193 1
0.6%
211 1
0.6%
232 1
0.6%
274 1
0.6%
317 1
0.6%
318 1
0.6%
357 1
0.6%
417 1
0.6%
468 1
0.6%
ValueCountFrequency (%)
996 2
1.3%
995 1
0.6%
994 1
0.6%
993 1
0.6%
990 1
0.6%
989 1
0.6%
985 1
0.6%
981 1
0.6%
980 1
0.6%
979 2
1.3%

Avg_Open_To_Buy
Real number (ℝ)

HIGH CORRELATION 

Distinct155
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5150.5759
Minimum2654
Maximum8959
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:50.706285image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2654
5-th percentile2846.3
Q13501.5
median4892.5
Q36528.75
95-th percentile8203.15
Maximum8959
Range6305
Interquartile range (IQR)3027.25

Descriptive statistics

Standard deviation1790.2754
Coefficient of variation (CV)0.34758742
Kurtosis-1.1267665
Mean5150.5759
Median Absolute Deviation (MAD)1450.5
Skewness0.3762689
Sum813791
Variance3205086
MonotonicityNot monotonic
2023-11-07T13:20:50.845959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3495 2
 
1.3%
2732 2
 
1.3%
6014 2
 
1.3%
8081 1
 
0.6%
6859 1
 
0.6%
2984 1
 
0.6%
5093 1
 
0.6%
4383 1
 
0.6%
3239 1
 
0.6%
2720 1
 
0.6%
Other values (145) 145
91.8%
ValueCountFrequency (%)
2654 1
0.6%
2691 1
0.6%
2720 1
0.6%
2732 2
1.3%
2768 1
0.6%
2786 1
0.6%
2831 1
0.6%
2849 1
0.6%
2858 1
0.6%
2862 1
0.6%
ValueCountFrequency (%)
8959 1
0.6%
8602 1
0.6%
8587 1
0.6%
8531 1
0.6%
8470 1
0.6%
8437 1
0.6%
8359 1
0.6%
8289 1
0.6%
8188 1
0.6%
8102 1
0.6%

Total_Amt_Chng_Q4_Q1
Real number (ℝ)

Distinct142
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.77031646
Minimum0.432
Maximum2.275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:50.971852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.432
5-th percentile0.50655
Q10.64775
median0.7505
Q30.85575
95-th percentile1.0435
Maximum2.275
Range1.843
Interquartile range (IQR)0.208

Descriptive statistics

Standard deviation0.21401101
Coefficient of variation (CV)0.2778222
Kurtosis16.530341
Mean0.77031646
Median Absolute Deviation (MAD)0.1035
Skewness2.8231936
Sum121.71
Variance0.045800715
MonotonicityNot monotonic
2023-11-07T13:20:51.114829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.647 3
 
1.9%
0.765 3
 
1.9%
0.815 2
 
1.3%
0.807 2
 
1.3%
0.681 2
 
1.3%
0.65 2
 
1.3%
0.676 2
 
1.3%
0.711 2
 
1.3%
0.85 2
 
1.3%
0.958 2
 
1.3%
Other values (132) 136
86.1%
ValueCountFrequency (%)
0.432 1
0.6%
0.456 1
0.6%
0.469 1
0.6%
0.471 1
0.6%
0.475 1
0.6%
0.485 1
0.6%
0.504 2
1.3%
0.507 1
0.6%
0.508 1
0.6%
0.511 1
0.6%
ValueCountFrequency (%)
2.275 1
0.6%
1.705 1
0.6%
1.32 1
0.6%
1.193 1
0.6%
1.084 1
0.6%
1.079 1
0.6%
1.072 1
0.6%
1.052 1
0.6%
1.042 1
0.6%
1.029 1
0.6%

Total_Trans_Amt
Real number (ℝ)

HIGH CORRELATION 

Distinct153
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4772.4684
Minimum791
Maximum16737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:51.240640image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum791
5-th percentile1346.5
Q12456.25
median3945.5
Q34857
95-th percentile14604.25
Maximum16737
Range15946
Interquartile range (IQR)2400.75

Descriptive statistics

Standard deviation3778.2852
Coefficient of variation (CV)0.79168365
Kurtosis2.7259701
Mean4772.4684
Median Absolute Deviation (MAD)1268
Skewness1.8761048
Sum754050
Variance14275439
MonotonicityNot monotonic
2023-11-07T13:20:51.398393image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3964 2
 
1.3%
4399 2
 
1.3%
4828 2
 
1.3%
14596 2
 
1.3%
1193 2
 
1.3%
4283 1
 
0.6%
5192 1
 
0.6%
2497 1
 
0.6%
4326 1
 
0.6%
4387 1
 
0.6%
Other values (143) 143
90.5%
ValueCountFrequency (%)
791 1
0.6%
842 1
0.6%
870 1
0.6%
990 1
0.6%
1165 1
0.6%
1193 2
1.3%
1321 1
0.6%
1351 1
0.6%
1359 1
0.6%
1438 1
0.6%
ValueCountFrequency (%)
16737 1
0.6%
16179 1
0.6%
15865 1
0.6%
15471 1
0.6%
15380 1
0.6%
15352 1
0.6%
14786 1
0.6%
14651 1
0.6%
14596 2
1.3%
14593 1
0.6%

Total_Trans_Ct
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.746835
Minimum22
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:51.540265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile28
Q149.25
median70
Q381
95-th percentile105.6
Maximum123
Range101
Interquartile range (IQR)31.75

Descriptive statistics

Standard deviation23.477306
Coefficient of variation (CV)0.34654469
Kurtosis-0.4133794
Mean67.746835
Median Absolute Deviation (MAD)15.5
Skewness0.052535701
Sum10704
Variance551.18391
MonotonicityNot monotonic
2023-11-07T13:20:51.669581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 8
 
5.1%
79 5
 
3.2%
59 5
 
3.2%
81 5
 
3.2%
67 4
 
2.5%
38 4
 
2.5%
76 4
 
2.5%
70 4
 
2.5%
82 4
 
2.5%
78 4
 
2.5%
Other values (62) 111
70.3%
ValueCountFrequency (%)
22 2
1.3%
24 2
1.3%
25 1
 
0.6%
26 1
 
0.6%
27 1
 
0.6%
28 2
1.3%
30 1
 
0.6%
31 2
1.3%
34 2
1.3%
35 4
2.5%
ValueCountFrequency (%)
123 1
 
0.6%
122 2
1.3%
120 1
 
0.6%
117 1
 
0.6%
115 1
 
0.6%
109 2
1.3%
105 3
1.9%
104 1
 
0.6%
102 2
1.3%
101 1
 
0.6%

Total_Ct_Chng_Q4_Q1
Real number (ℝ)

Distinct126
Distinct (%)79.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69213291
Minimum0.231
Maximum1.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:51.811912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.231
5-th percentile0.37125
Q10.5795
median0.669
Q30.78825
95-th percentile1.083
Maximum1.5
Range1.269
Interquartile range (IQR)0.20875

Descriptive statistics

Standard deviation0.20818176
Coefficient of variation (CV)0.30078292
Kurtosis2.0395173
Mean0.69213291
Median Absolute Deviation (MAD)0.099
Skewness0.84649393
Sum109.357
Variance0.043339645
MonotonicityNot monotonic
2023-11-07T13:20:51.976707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.694 3
 
1.9%
0.75 3
 
1.9%
0.64 3
 
1.9%
0.581 3
 
1.9%
0.667 3
 
1.9%
0.733 3
 
1.9%
0.652 3
 
1.9%
0.723 3
 
1.9%
0.556 2
 
1.3%
0.757 2
 
1.3%
Other values (116) 130
82.3%
ValueCountFrequency (%)
0.231 1
0.6%
0.273 1
0.6%
0.29 1
0.6%
0.3 1
0.6%
0.308 1
0.6%
0.333 1
0.6%
0.346 1
0.6%
0.35 1
0.6%
0.375 1
0.6%
0.4 2
1.3%
ValueCountFrequency (%)
1.5 1
0.6%
1.417 1
0.6%
1.273 1
0.6%
1.258 1
0.6%
1.2 1
0.6%
1.147 1
0.6%
1.121 1
0.6%
1.083 2
1.3%
1.034 1
0.6%
0.977 1
0.6%

Avg_Utilization_Ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)68.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14063924
Minimum0.016
Maximum0.267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:52.163746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.016
5-th percentile0.061
Q10.10225
median0.1355
Q30.181
95-th percentile0.2353
Maximum0.267
Range0.251
Interquartile range (IQR)0.07875

Descriptive statistics

Standard deviation0.05241204
Coefficient of variation (CV)0.3726701
Kurtosis-0.45011028
Mean0.14063924
Median Absolute Deviation (MAD)0.039
Skewness0.24741803
Sum22.221
Variance0.0027470219
MonotonicityNot monotonic
2023-11-07T13:20:52.302500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.126 4
 
2.5%
0.158 4
 
2.5%
0.186 3
 
1.9%
0.216 3
 
1.9%
0.189 3
 
1.9%
0.191 3
 
1.9%
0.104 3
 
1.9%
0.144 3
 
1.9%
0.122 3
 
1.9%
0.195 3
 
1.9%
Other values (98) 126
79.7%
ValueCountFrequency (%)
0.016 1
0.6%
0.039 1
0.6%
0.04 1
0.6%
0.042 1
0.6%
0.05 1
0.6%
0.053 1
0.6%
0.055 1
0.6%
0.061 2
1.3%
0.063 1
0.6%
0.067 1
0.6%
ValueCountFrequency (%)
0.267 1
0.6%
0.262 1
0.6%
0.26 1
0.6%
0.255 1
0.6%
0.241 1
0.6%
0.24 1
0.6%
0.238 1
0.6%
0.237 1
0.6%
0.235 1
0.6%
0.225 1
0.6%
Distinct123
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1955359
Minimum2.0889 × 10-5
Maximum0.99893
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:52.431662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.0889 × 10-5
5-th percentile3.58496 × 10-5
Q19.350925 × 10-5
median0.00019864
Q30.00050046
95-th percentile0.9971765
Maximum0.99893
Range0.99890911
Interquartile range (IQR)0.00040695075

Descriptive statistics

Standard deviation0.39663583
Coefficient of variation (CV)2.0284552
Kurtosis0.39098319
Mean0.1955359
Median Absolute Deviation (MAD)0.000127414
Skewness1.5447176
Sum30.894673
Variance0.15731998
MonotonicityNot monotonic
2023-11-07T13:20:52.573216image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00019864 5
 
3.2%
0.00018665 4
 
2.5%
0.99499 4
 
2.5%
0.00031104 3
 
1.9%
0.00017987 3
 
1.9%
0.99683 3
 
1.9%
0.00011382 3
 
1.9%
7.1226 × 10-52
 
1.3%
0.00011937 2
 
1.3%
4.7263 × 10-52
 
1.3%
Other values (113) 127
80.4%
ValueCountFrequency (%)
2.0889 × 10-51
0.6%
2.1081 × 10-51
0.6%
2.2331 × 10-51
0.6%
3.1176 × 10-52
1.3%
3.3214 × 10-52
1.3%
3.4833 × 10-51
0.6%
3.6029 × 10-51
0.6%
4.3568 × 10-51
0.6%
4.7263 × 10-52
1.3%
5.1697 × 10-51
0.6%
ValueCountFrequency (%)
0.99893 1
0.6%
0.99873 1
0.6%
0.99844 1
0.6%
0.99838 1
0.6%
0.99821 1
0.6%
0.99807 1
0.6%
0.99741 1
0.6%
0.99727 1
0.6%
0.99716 1
0.6%
0.99694 1
0.6%
Distinct69
Distinct (%)43.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.80446381
Minimum0.00106612
Maximum0.99998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-11-07T13:20:52.722192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.00106612
5-th percentile0.0028257355
Q10.9994975
median0.9998
Q30.9999075
95-th percentile0.9999615
Maximum0.99998
Range0.99891388
Interquartile range (IQR)0.00041

Descriptive statistics

Standard deviation0.3966355
Coefficient of variation (CV)0.49304331
Kurtosis0.39098316
Mean0.80446381
Median Absolute Deviation (MAD)0.00013
Skewness-1.5447176
Sum127.10528
Variance0.15731972
MonotonicityNot monotonic
2023-11-07T13:20:52.847019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.99993 10
 
6.3%
0.9998 10
 
6.3%
0.99989 8
 
5.1%
0.99969 7
 
4.4%
0.99991 7
 
4.4%
0.99994 6
 
3.8%
0.9999 6
 
3.8%
0.99981 5
 
3.2%
0.99982 5
 
3.2%
0.99983 5
 
3.2%
Other values (59) 89
56.3%
ValueCountFrequency (%)
0.00106612 1
0.6%
0.00127462 1
0.6%
0.00155735 1
0.6%
0.00162275 1
0.6%
0.00179304 1
0.6%
0.00193323 1
0.6%
0.00258725 1
0.6%
0.00272694 1
0.6%
0.00284317 1
0.6%
0.00305523 1
0.6%
ValueCountFrequency (%)
0.99998 3
 
1.9%
0.99997 5
3.2%
0.99996 2
 
1.3%
0.99995 4
 
2.5%
0.99994 6
3.8%
0.99993 10
6.3%
0.99992 3
 
1.9%
0.99991 7
4.4%
0.9999 6
3.8%
0.99989 8
5.1%

Interactions

2023-11-07T13:20:44.122448image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:22.017844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:23.515940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:24.945890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:26.324538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:27.827283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:29.210744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:30.626408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:32.067545image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:33.631915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:35.083662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:36.532178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:38.044098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:39.570247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:41.002954image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:42.489532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:44.239205image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:22.106085image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:23.596512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:25.035574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:26.404976image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:27.890677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:29.309132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:30.707408image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:32.163236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:33.727409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:35.175588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:36.628191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:38.124068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:39.657634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:41.090638image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:42.585712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:44.387018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:22.208177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:23.692278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:25.125299image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:26.491875image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:27.983850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:29.398286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:30.803509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:32.259098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:33.815809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:35.264985image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:36.725836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:38.212603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:39.738424image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:41.179439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:42.673721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:44.526392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:22.296520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:23.780572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:25.199661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:26.572492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:28.071943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:29.478496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:30.891366image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:32.339456image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:33.904389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:35.361331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:36.815913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:38.301574image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:39.834005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:41.266561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:42.770032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:44.647631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:22.377644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:23.864321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:25.279415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:26.645454image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:28.150481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:29.558350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:30.965986image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:32.420585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:33.984592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:35.448796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:36.896546image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:38.373983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:39.914447image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:41.348027image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:42.865310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:44.734992image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:22.558352image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:23.950840image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:25.358562image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:26.726917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:28.231059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:29.640650image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:31.058514image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:32.508948image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:34.082389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:35.529626image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:36.993002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:38.478491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:40.006199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:41.427996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:42.953683image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:44.831337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:22.637440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:24.032969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:25.447038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:26.808204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:28.319365image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:29.721105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:31.138918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:32.590528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:34.173796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:35.624150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:37.089538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:38.559319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:40.091887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:41.516778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:43.066047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:44.927407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:22.716372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:24.116778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:25.527284image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:26.880213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:28.407917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:29.809432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:31.226489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:32.680160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:34.263185image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:35.708972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:37.194265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:38.640893image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:40.182735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:41.607646image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:43.172857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:45.046814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:22.796528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:24.206048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:25.616581image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:26.960588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:28.488311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:29.883674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:31.330694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:32.752334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:34.342997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:35.792370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:37.282228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:38.730253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:40.262726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:41.680279image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:43.269742image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:45.174262image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:22.885326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:24.293470image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:25.705017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:27.049201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:28.576973image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:29.975674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:31.415592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:32.842172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:34.431623image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:35.884385image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:37.370890image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:38.810886image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:40.350845image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:41.768547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:43.372641image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:45.302863image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:22.974152image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:24.383044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:25.794556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:27.170971image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:28.666218image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:30.062843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:31.515242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:32.940735image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:34.527850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:35.977910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:37.459779image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:39.025534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:40.448247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:41.894815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:43.485232image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:45.551422image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:23.066068image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:24.479075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:25.883070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:27.271331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:28.761982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:30.175919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:31.611003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:33.037008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:34.625594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:36.082005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:37.564917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:39.122104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:40.553191image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:42.003749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:43.597113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:45.645438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:23.153569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:24.567603image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:25.970680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:27.346178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:28.841335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:30.254725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:31.699746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:33.125787image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:34.712331image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:36.162525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:37.652798image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:39.202464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:40.633184image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:42.101236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:43.685955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:45.774671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:23.233838image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:24.665781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:26.058592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:27.442431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:28.930179image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:30.344338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:31.788420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:33.207788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:34.807587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:36.258117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:37.748699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:39.290257image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:40.721469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:42.206949image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:43.790204image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:45.864425image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:23.321899image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:24.753229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:26.140852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:27.521634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:29.018303image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:30.432586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:31.876354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:33.422570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:34.895839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:36.340228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:37.836670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:39.372070image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:40.810430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:42.295247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:43.886652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:46.015655image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:23.411066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:24.849852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:26.229701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:27.609114image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:29.115582image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:30.529668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:31.975587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:33.543541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:34.992050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:36.444278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:37.931990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:39.473223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:40.898880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:42.384087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:20:43.982960image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-07T13:20:52.972511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
CLIENTNUMCustomer_AgeDependent_countMonths_on_bookMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioNaive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2Attrition_FlagGenderEducation_LevelMarital_StatusIncome_CategoryTotal_Relationship_Count
CLIENTNUM1.0000.111-0.1850.205-0.0410.127-0.052-0.188-0.0380.0350.0060.018-0.012-0.0400.074-0.0710.0000.1020.0000.0990.0730.000
Customer_Age0.1111.000-0.3220.801-0.0510.039-0.107-0.080-0.1020.012-0.003-0.0590.0380.073-0.0240.0220.1120.0000.0950.0550.0000.109
Dependent_count-0.185-0.3221.000-0.2520.078-0.0070.003-0.0260.0040.0180.1630.0830.035-0.0350.161-0.1570.1650.0000.0690.0540.0930.000
Months_on_book0.2050.801-0.2521.0000.0020.023-0.141-0.131-0.1300.0130.0860.004-0.0020.077-0.0170.0160.0000.0000.0000.1640.0460.000
Months_Inactive_12_mon-0.041-0.0510.0780.0021.0000.1360.001-0.0550.0050.099-0.010-0.023-0.095-0.0460.549-0.5510.3170.0630.0000.0000.0000.073
Contacts_Count_12_mon0.1270.039-0.0070.0230.1361.000-0.108-0.153-0.0930.071-0.065-0.075-0.0120.0130.693-0.6930.1970.1080.0730.0000.0000.000
Credit_Limit-0.052-0.1070.003-0.1410.001-0.1081.0000.1010.9930.016-0.100-0.080-0.195-0.775-0.0860.0880.0000.2690.0000.0000.1720.037
Total_Revolving_Bal-0.188-0.080-0.026-0.131-0.055-0.1530.1011.000-0.0030.0590.1070.1830.0940.489-0.2490.2490.5640.0000.1350.1240.0390.097
Avg_Open_To_Buy-0.038-0.1020.004-0.1300.005-0.0930.993-0.0031.0000.005-0.117-0.107-0.216-0.839-0.0550.0570.0000.2390.0000.0000.1110.013
Total_Amt_Chng_Q4_Q10.0350.0120.0180.0130.0990.0710.0160.0590.0051.0000.0720.0070.3050.0290.086-0.0820.0900.2790.0000.0000.1690.000
Total_Trans_Amt0.006-0.0030.1630.086-0.010-0.065-0.1000.107-0.1170.0721.0000.8810.2610.158-0.2110.2140.4020.0980.0780.0000.0000.204
Total_Trans_Ct0.018-0.0590.0830.004-0.023-0.075-0.0800.183-0.1070.0070.8811.0000.2560.200-0.2940.2960.5730.0000.0000.1260.0000.141
Total_Ct_Chng_Q4_Q1-0.0120.0380.035-0.002-0.095-0.012-0.1950.094-0.2160.3050.2610.2561.0000.248-0.2000.1980.2760.1790.0570.0210.0000.022
Avg_Utilization_Ratio-0.0400.073-0.0350.077-0.0460.013-0.7750.489-0.8390.0290.1580.2000.2481.000-0.1150.1150.4160.2230.0720.2630.1770.134
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_10.074-0.0240.161-0.0170.5490.693-0.086-0.249-0.0550.086-0.211-0.294-0.200-0.1151.000-0.9990.9800.0000.0000.0000.1630.230
Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2-0.0710.022-0.1570.016-0.551-0.6930.0880.2490.057-0.0820.2140.2960.1980.115-0.9991.0000.9800.0000.0000.0000.1630.230
Attrition_Flag0.0000.1120.1650.0000.3170.1970.0000.5640.0000.0900.4020.5730.2760.4160.9800.9801.0000.0000.0000.0000.1630.230
Gender0.1020.0000.0000.0000.0630.1080.2690.0000.2390.2790.0980.0000.1790.2230.0000.0000.0001.0000.0000.1490.7920.000
Education_Level0.0000.0950.0690.0000.0000.0730.0000.1350.0000.0000.0780.0000.0570.0720.0000.0000.0000.0001.0000.0270.0000.000
Marital_Status0.0990.0550.0540.1640.0000.0000.0000.1240.0000.0000.0000.1260.0210.2630.0000.0000.0000.1490.0271.0000.0000.000
Income_Category0.0730.0000.0930.0460.0000.0000.1720.0390.1110.1690.0000.0000.0000.1770.1630.1630.1630.7920.0000.0001.0000.000
Total_Relationship_Count0.0000.1090.0000.0000.0730.0000.0370.0970.0130.0000.2040.1410.0220.1340.2300.2300.2300.0000.0000.0000.0001.000

Missing values

2023-11-07T13:20:46.215580image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-07T13:20:46.559785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioNaive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2
38715190283Existing Customer57F1GraduateUnknown$40K - $60KBlue493323672.08862786.01.3201464280.5560.2410.0001690.999830
58711427458Existing Customer44F5GraduateMarriedUnknownBlue354126273.09785295.02.2751359251.0830.1560.0000570.999940
258714187533Existing Customer45F2UneducatedSingle$40K - $60KBlue352303540.08492691.00.4561321280.7500.2400.0000680.999930
509716223708Attrited Customer45F3UneducatedSingleUnknownBlue363334028.07103318.00.731791220.8330.1760.9969100.003088
573715416633Existing Customer54M1UnknownMarried$80K - $120KBlue364126175.09605215.00.7091847590.4050.1550.0000570.999940
622711628458Existing Customer45F3GraduateMarriedLess than $40KBlue314108829.09017928.00.8251902450.6670.1020.0000210.999980
716789291633Existing Customer62M0GraduateMarried$60K - $80KBlue513205450.09674483.00.7011526450.8000.1770.0000360.999960
771715271733Existing Customer50F2UneducatedSingleLess than $40KBlue373308520.08147706.00.5041193350.3460.0960.0000680.999930
950788814783Existing Customer39M1UneducatedSingle$60K - $80KBlue224339204.08458359.00.6731820580.6110.0920.0002920.999710
1005772532208Attrited Customer51F2DoctorateMarriedLess than $40KBlue412236630.09165714.00.927842240.5000.1380.9964000.003596
CLIENTNUMAttrition_FlagCustomer_AgeGenderDependent_countEducation_LevelMarital_StatusIncome_CategoryCard_CategoryMonths_on_bookTotal_Relationship_CountMonths_Inactive_12_monContacts_Count_12_monCredit_LimitTotal_Revolving_BalAvg_Open_To_BuyTotal_Amt_Chng_Q4_Q1Total_Trans_AmtTotal_Trans_CtTotal_Ct_Chng_Q4_Q1Avg_Utilization_RatioNaive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2
9834788019483Existing Customer49M3GraduateMarried$80K - $120KBlue404133660.07662894.00.535145151220.8480.2090.0000960.99990
9864720633558Existing Customer37F3GraduateDivorcedLess than $40KBlue243343727.09952732.00.743147861010.6560.2670.0005350.99946
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